• Build, maintain, and improve dashboards and reports that give stakeholders clear, timely visibility into performance. • Write and optimise SQL and Python to extract, transform, and analyse data from multiple sources. • Partner with stakeholders to understand their questions, define metrics, and translate requirements into analytics that drive decisions. • Ensure data quality and accuracy through validation, de-duplication, and reconciliation of reporting outputs. • Document analyses and reporting logic, and present findings clearly to both technical and non-technical audiences.

Role Description At Zingbrain, we build real-time personalization systems for iGaming platforms. Our models operate in production, influencing what each user sees — from game recommendations to sportsbook event suggestions — based on live behavioral, transactional, and contextual data. We’re looking for a Senior Data Scientist to join our team and help us in the following areas: - Develop ML-driven features for casino games using supervised learning (regression, ranking, classification) - Maintain and enhance the existing recommendation systems in production, including: - Model enhancement using gradient boosting methods - Data cleaning and preprocessing - Pre- and post-processing workflows - Optimization of training and inference pipelines - Integration of ML models into Airflow pipelines in a multi-tenant environment - Adopt and configure the solution for different clients (tenants) This is a hands-on role involving modeling, experimentation, and close collaboration with engineering and product teams in a high-load, real-time environment. As a part of our team you will: - Collaborate with cross-functional teams of data scientists, engineers, product owners, designers, and researchers to ensure project success - Analyze large datasets to extract actionable insights that inform product decisions - Propose, implement, and evaluate machine learning approaches to solve business problems, working closely with Product Owner(s) - Maintain and adopt the current recommendation solution in a multi-tenant environment - Influence product strategy through research and experimentation that deepens understanding of how product features, platforms, and promotions affect user behavior Qualifications - 5+ years of experience in data science - A degree in a quantitative field (e.g., Mathematics, Statistics, Computer Science) Requirements - Proficiency in Python, SQL, and data manipulation tools (Pandas, Polars) - Strong engineering skills to design, build, and maintain scalable ML solutions, including implementing observability across pipelines through metrics, logging, and alerting - Knowledge of Docker, Kubernetes - Ability to structure and solve loosely defined problems, delivering actionable insights for product development; Strong analytical mindset with both numerical and business understanding - Hands-on experience with supervised ML techniques (regression and ranking using XGBoost, LightGBM, CatBoost, or neural networks), including feature engineering, model evaluation (AUC, NDCG, MSE, uplift metrics), and personalization or recommendation systems - Proven experience deploying ML models to production for near real-time or batch processing - Solid knowledge of statistical methods (A/B testing, significance testing, etc.) Benefits - Wellness program - Medical compensation - Paid sick leaves - Compensation for sports activities - Well-being webinars and workshops - Work & life balance - Wellness Day: 4th Friday off monthly - Remote work - 21 working days of vacation - 5 personal days per year - Professional development - English speaking club - Language learning bonus €150 per month - 80% paid professional employee training - Provided tech equipment - €150 for the arrangement of the workplace - Bonuses for significant events and additional personal days if necessary - Offline and online company parties and team buildings

• Lead and manage a team of senior associates and associates within the Banking/Investing vertical, driving team performance, productivity, and adherence to KPIs, identifying training needs and support capability building within the team • While performing research, oversee end-to-end project execution within the Banking/Investing domain • Ensure timely delivery of high-quality research • Develop and share subject knowledge expertise with team members, to help them navigate complex projects and act as a Subject Matter Expert (SME) for domain-specific queries • Support onboarding and training new team members, conduct knowledge-sharing sessions on Banking/Investing metrics • Develop and maintain SOPs, guidelines, and knowledge of repositories • Collaborate with analysts, to understand requirements and align on deliverables • Provide regular updates on progress, challenges, and key insights • Track and evaluate team performance metrics, provide regular feedback to members in areas to improve, ensure timely delivery of projects with high accuracy (target: ≥97%) • Proactively manage escalations and resolve delivery challenges • Drive continuous improvement initiatives within the team, identify opportunities to improve workflows, and optimize processes

Role Description Swish Analytics is looking for a Soccer Data Scientist to join our ever-growing team! Data Science is at the core of our business, so this team has true ownership and impact over developing core components of Swish's data products. This position is remote from the USA. - Ideate, develop and improve machine learning and statistical models that drive Swish’s core algorithms for producing state-of-the-art sports betting products. - Develop contextualized feature sets using sports specific domain knowledge. - Contribute to all stages of model development, from creating proof-of-concepts and beta testing, to partnering with data engineering and product teams to deploy new models. - Strive to constantly improve model performance using insights from rigorous offline and online experimentation. - Analyze results and outputs to assess model performance and identify model weaknesses for directing development efforts. - Adhere to software engineering best practices and contribute to shared code repositories. - Document modeling work and present to stakeholders and other technical and non-technical partners. Qualifications - Masters degree in Data Analytics, Data Science, Computer Science or related technical subject area. - Demonstrated experience developing models at production scale for Soccer, or sports betting for 2+ years. - Expertise in Probability Theory, Machine Learning, Inferential Statistics, Bayesian Statistics, Markov Chain Monte Carlo methods. - 5+ years of demonstrated experience developing and delivering effective machine learning and/or statistical models to serve business needs in sports or sports betting. - Experience with relational SQL & Python. - Experience with source control tools such as GitHub and related CI/CD processes. - Experience working in AWS environments. - Proven track record of strong leadership skills. Has shown ability to partner with teams in solving complex problems by taking a broad perspective to identify innovative solutions. - Excellent communication skills to both technical and non-technical audiences. Requirements - Base Salary: Starting at $130,000. Company Description Swish Analytics is a sports analytics, betting and fantasy startup building the next generation of predictive sports analytics data products. We believe that oddsmaking is a challenge rooted in engineering, mathematics, and sports betting expertise; not intuition. We're looking for team-oriented individuals with an authentic passion for accurate and predictive real-time data who can execute in a fast-paced, creative, and continually-evolving environment without sacrificing technical excellence. Our challenges are unique, so we hope you are comfortable in uncharted territory and passionate about building systems to support products across a variety of industries and consumer/enterprise clients.